Within an image, pixels have both a position and a value. In the sections above all the measurements involved position (see Position measurements in pixels or Position measurements in WCS). The measurements in this section only deal with pixel values and ignore the pixel positions completely. In other words, for the options of this section each labeled region within the input is just a group of values (and their associated error values if necessary), and they let you do various types of measurements on the resulting distribution of values. For more on the difference between the --*error or --*std columns see Standard deviation vs Standard error.
The sum of all pixel values associated to this label (object or clump). Note that if a sky value or image has been given, it will be subtracted before any column measurement. For clumps, the ambient values (average of river pixels around the clump, multiplied by the area of the clump) is subtracted, see --river-mean. So the sum of all the clump-sums in the clump catalog of one object will be smaller than the --clumps-sum column of the objects catalog.
If no usable pixels are present over the clump or object (for example, they are all blank), the returned value will be NaN (note that zero is meaningful).
The standard deviation of the sum of values of a label (objects or clumps). The value is calculated by using the values image (for signal above the sky level) and the sky standard deviation image (extension --stdhdu of file given to --instd); which you can derive for any image using NoiseChisel. This column is internally used to measure the signal-to-noise (--sn).
For objects this is calculated by adding the sky variance (second power of the sky standard deviation image) of each pixel in the label, with the value of the pixel if the value is not negative (error only increases). This is done to account for brighter pixels which have higher noise in the Poisson distribution (its side effect is that the error will always be slightly over-estimated due to the positive values close to the noise). A correction may be applied if the sky standard deviation is negative; see Section 3.3 of Akhlaghi & Ichikawa 2015. For clumps, the variance of the rivers (which are subtracted from the value of pixels in calculating the sum) are also added to generate the final standard deviation.
The returned value will be NaN when the label covers only NaN pixels in the values image, or a pixel is NaN in the --instd image, but non-NaN in the values image. The latter situation usually happens when there is a bug in the previous steps of your analysis. This is because the sky standard deviation should have a value in all pixels. In such cases, it is important to find the cause and fix it because those pixels with a NaN in the --instd image may contribute significantly to the final error. If you want to ignore those pixels in the error measurement, set them to zero (which is a meaningful number in such scenarios).
[Objects] The total sum of the pixels covered by clumps (before subtracting the river) within each object. This is simply the sum of --sum-no-river in the clumps catalog (see below). If no usable pixels are present over the clump or object (for example, they are all blank), the stored value will be NaN (note that zero is meaningful).
[Clumps] The sum of Sky (not river) subtracted clump pixel values. By definition, for the clumps, the average value of the rivers surrounding it are subtracted from it for a first order accounting for contamination by neighbors.
If no usable pixels are present over the clump or object (for example, they are all blank), the stored value will be NaN (note that zero is meaningful).
The mean sky subtracted value of pixels within the object or clump. For clumps, the average river flux is subtracted from the sky subtracted mean.
The error in measuring the mean; using both the values file and the sky standard deviation image. In case the given standard deviation or variance image already contains the contributions from the pixel values (it is not just the sky standard deviation), use --novalinerror).
The standard deviation of the pixels within the object or clump. For clumps, the river pixels are not subtracted because that is a constant (per pixel) value and should not affect the standard deviation.
The median sky subtracted value of pixels within the object or clump. For clumps, the average river flux is subtracted from the sky subtracted median.
The maximum value of pixels within the object or clump. When the label (object or clump) is larger than three pixels, the maximum is actually derived by the mean of the brightest three pixels, not the largest pixel value of the same label. This is because noise fluctuations can be very strong in the extreme values of the objects/clumps due to Poisson noise (which gets stronger as the mean gets higher). Simply using the maximum pixel value will create a strong scatter in results that depend on the maximum (for example, the --fwhm option also uses this value internally).
The number of elements/pixels in the dataset after sigma-clipping the object or clump. The sigma-clipping parameters can be set with the --sigmaclip option described in MakeCatalog inputs and basic settings. For more on Sigma-clipping, see Sigma clipping.
The sigma-clipped median value of the object of clump’s pixel distribution. For more on sigma-clipping and how to define it, see --sigclip-number.
The sigma-clipped mean value of the object of clump’s pixel distribution. For more on sigma-clipping and how to define it, see --sigclip-number.
The sigma-clipped standard deviation of the object of clump’s pixel distribution. For more on sigma-clipping and how to define it, see --sigclip-number.
The magnitude of clumps or objects. It is derived through the pixel counts over the label (which you can see in the --sum column) and the value given to the --zeropoint through the definition of the magnitude described in Brightness, Flux, Magnitude and Surface brightness.
The magnitude error of clumps or objects. The method used is described below. As we see there, this error assumes uncorrelated pixel values and also does not include the error in estimating the aperture (or error in generating the labeled image). See the status of implementation of such factors in Task 14124.
The returned value will be NaN when the label covers only NaN pixels in the values image, or a pixel is NaN in the --instd image, but non-NaN in the values image. The latter situation usually happens when there is a bug in the previous steps of your analysis, and is important because those pixels with a NaN in the --instd image may contribute significantly to the final error. If you want to ignore those pixels in the error measurement, set them to zero (which is a meaningful number in such scenarios).
The raw error in measuring the magnitude is only meaningful when the object’s magnitude is brighter than the upper-limit magnitude (see below). As discussed in Brightness, Flux, Magnitude and Surface brightness, the magnitude (\(M\)) of an object with brightness \(B\) and zero point magnitude \(z\) can be written as:
$$M=-2.5\log_{10}(B)+z$$
Calculating the derivative with respect to \(B\), we get:
$${dM\over dB} = {-2.5\over {B\times ln(10)}}$$
From the Tailor series (\(\Delta{M}=dM/dB\times\Delta{B}\)), we can write:
$$\Delta{M} = \left|{-2.5\over ln(10)}\right|\times{\Delta{B}\over{B}}$$
But, \(\Delta{B}/B\) is just the inverse of the Signal-to-noise ratio (\(S/N\)), so we can write the error in magnitude in terms of the signal-to-noise ratio:
$$\Delta{M} = {2.5\over{S/N\times ln(10)}} $$
MakeCatalog uses this relation to estimate the magnitude errors. The signal-to-noise ratio is calculated in different ways for clumps and objects, see Akhlaghi and Ichikawa 2015), but this single equation can be used to estimate the measured magnitude error afterwards for any type of target.
[Objects] The magnitude of all clumps in this object, see --clumps-sum.
[Clumps] The average of the river pixel values around this clump. River pixels were defined in Akhlaghi and Ichikawa 2015. In short they are the pixels immediately outside of the clumps. This value is used internally to find the sum (or magnitude) and signal to noise ratio of the clumps. It can generally also be used as a scale to gauge the base (ambient) flux surrounding the clump. In case there was no river pixels, then this column will have the value of the Sky under the clump. So note that this value is not sky subtracted.
[Clumps] The number of river pixels around this clump, see --river-mean.
[Clumps] Minimum river value around this clump, see --river-mean.
[Clumps] Maximum river value around this clump, see --river-mean.
The Signal to noise ratio (S/N) of all clumps or objects. See Akhlaghi and Ichikawa (2015) for the exact equations used.
The returned value will be NaN when the label covers only NaN pixels in the values image, or a pixel is NaN in the --instd image, but non-NaN in the values image. The latter situation usually happens when there is a bug in the previous steps of your analysis, and is important because those pixels with a NaN in the --instd image may contribute significantly to the final error. If you want to ignore those pixels in the error measurement, set them to zero (which is a meaningful number).
The sky flux (per pixel) value under this object or clump. This is actually the mean value of all the pixels in the sky image that lie on the same position as the object or clump.
The sky value standard deviation (per pixel) for this clump or object. This is the square root of the mean variance under the object, or the root mean square.
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GNU Astronomy Utilities 0.24 manual, November 2025.